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Title	Edge-Cloud Collaborative Computation Offloading For Federated Learning In Smart City
ID_Doc	21825
Authors	Peng K.; Zhang H.; Zhao B.; Liu P.
Year	2022
Published	Proceedings of the 2022 IEEE International Conference on Dependable, Autonomic and Secure Computing, International Conference on Pervasive Intelligence and Computing, International Conference on Cloud and Big Data Computing, International Conference on Cyber Science and Technology Congress, DASC/PiCom/CBDCom/CyberSciTech 2022
DOI	http://dx.doi.org/10.1109/DASC/PiCom/CBDCom/Cy55231.2022.9927848
Abstract	With the continuous transformation and development of information technologies, the smart city is becoming a promising paradigm to deal with the enormous network data. Among them, federated learning technology has emerged as a key tool for intelligent analysis and data processing. It effectively guarantees the privacy and security of users by shifting conventional data storage and model training to local devices. Nevertheless, strong convergence performance necessitates numerous rounds of data exchange, which is uneconomical for edge platforms with limited resources. In view of this, we study the offloading of federated learning models in the edge-cloud collaborative smart city. Firstly, we transform FL models into a helpful structure for improving the efficient aggregation of servers. Then, to lower the energy and time cost of mobile devices throughout the model transmission and aggregation process while maintaining a high resource utilization level of edge servers, we develop an efficient computational offloading mechanism. Finally, the experimental results demonstrate the efficiency of our proposed method. © 2022 IEEE.
Author Keywords	Computing Offloading; Dynamic Resource Management; Federated Learning; Mobile Edge Computing; Smart City

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